Papers by Patrick Nguyen
CaLcs: Continuously Approximating Longest Common Subsequence for Sequence Level Optimization (D18-1)
Copied to clipboard
| Challenge: | Maximum-likelihood estimation (MLE) is widely used for text-generation based natural language processing applications. |
| Approach: | They propose a method to train models with maximum-likelihood estimation using a differentiable surrogate of longest common subsequence measure that captures sequence-level structure similarity. |
| Outcome: | Experimental results show that the proposed approach improves on the current MLE approach for downstream tasks like text summarization and machine translation. |